The present invention relates to segmenting multiple organs in medical images more particularly, to joint segmentation of multiple organs by fusing local and global context.
Algorithms for segmenting anatomical structures in medical images are typically targeted to segmenting individual structures. When the problem is posed as the joint segmentation of multiple organs, constraints, such as a non-overlapping constraint, may be formulated between the organs, and the combined formulation allows for a richer prior model on the joint shape of the multiple structures of interest. Such multi-organ segmentation is typically posed with atlas-based or level set-based formulations due to the ease in which geometric constraints can be modeled using such formulations.
However, level set methods are computationally demanding and typically require an accurate initialization so as not to fall into a local minimum. Discriminative learning-based methods are an alternative approach to such level set segmentations, but learning-based methods typically treat the initialization of each organ as an independent problem. While solving a single organ segmentation problem with learning-based methods can be fast, in order to achieve multi-object segmentation, a tree-like search structure has to be imposed on the detection order of the structures, resulting in a decrease in efficiency.